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#set up path
knitr::opts_knit$set(root.dir = normalizePath("/Users/mishafaruki/Desktop/QBIO490/qbio_490_mishafaruki/analysis_data")) #import libraries
library(BiocManager)Bioconductor version 3.18 (BiocManager 1.30.22), R 4.3.2 (2023-10-31)library(TCGAbiolinks)Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
Registered S3 method overwritten by 'data.table':
method from
print.data.table library(maftools)
#create a query based on the clinical category of TCGA-SKCM
clin_query <- GDCquery(project = "TCGA-SKCM",
data.category = "Clinical",
data.type = "Clinical Supplement",
data.format = 'BCR Biotab')--------------------------------------
o GDCquery: Searching in GDC database
--------------------------------------
Genome of reference: hg38
--------------------------------------------
oo Accessing GDC. This might take a while...
--------------------------------------------
ooo Project: TCGA-SKCM
--------------------
oo Filtering results
--------------------
ooo By data.format
ooo By data.type
----------------
oo Checking data
----------------
ooo Checking if there are duplicated cases
Warning: There are more than one file for the same case. Please verify query results. You can use the command View(getResults(query)) in rstudio
ooo Checking if there are results for the query
-------------------
o Preparing output
------------------- GDCdownload(clin_query)Downloading data for project TCGA-SKCM
Of the 6 files for download 6 already exist.
All samples have been already downloaded clinical.BCRtab.all <- GDCprepare(clin_query)
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|================================================================================================================| 100%#create a query based on the Transcriptome Profiling/RNA category of TCGA-SKCM
rna_query <- GDCquery(project ="TCGA-SKCM",
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "STAR - Counts")--------------------------------------
o GDCquery: Searching in GDC database
--------------------------------------
Genome of reference: hg38
--------------------------------------------
oo Accessing GDC. This might take a while...
--------------------------------------------
ooo Project: TCGA-SKCM
--------------------
oo Filtering results
--------------------
ooo By data.type
ooo By workflow.type
----------------
oo Checking data
----------------
ooo Checking if there are duplicated cases
ooo Checking if there are results for the query
-------------------
o Preparing output
-------------------GDCdownload(rna_query)Downloading data for project TCGA-SKCM
Of the 473 files for download 473 already exist.
All samples have been already downloadedrna_se <- GDCprepare(rna_query)
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|==============================================================================================|100% Completed after 33 s Starting to add information to samples
=> Add clinical information to samples
=> Adding TCGA molecular information from marker papers
=> Information will have prefix 'paper_'
skcm subtype information from:doi:10.1016/j.cell.2015.05.044
Available assays in SummarizedExperiment :
=> unstranded
=> stranded_first
=> stranded_second
=> tpm_unstrand
=> fpkm_unstrand
=> fpkm_uq_unstrand- Difference in survival between metastatic and non-metastatic patients (KM plot)
# load required packages for KM plot
if (!require(survival)) {
install.packages("survival")
}
if (!require(survminer)) {
install.packages("survminer")
}
if (!require(ggplot2)) {
install.packages("ggplot2")
}
library(survival)
library(survminer)
library(ggplot2)
#create rna clinical that will contain data from colData
rna_clinical = rna_se@colData
#convert it to a dataframe
rna_clinical <- as.data.frame(rna_clinical)
#Set output to Tumor_Sample_Barcode in rna_clincal so that it derived from patient category
rna_clinical$Tumor_Sample_Barcode <- rna_clinical$patient
#Set output to vital_status_num have to contain vital_status colum's data but make the values numeric
rna_clinical$vital_status_num <- as.numeric(rna_clinical$vital_status == "Dead")
#Create Survminer Object
#contains the survival data
survival_object <- Surv(time = rna_clinical$days_to_death, event = rna_clinical$vital_status_num)
#fit survival_object curve
fit <- survfit(surv_obj ~ rna_clinical$definition, data = rna_clinical)
#create KM plot
ggsurvplot(fit, data = rna_clinical, pval = TRUE,
risk.table = TRUE, #risk table
legend.title = "Metastatic Status",
xlab = "Time", ylab = "Survival probability",
break.time.by = 1000, # adjusting time intervals
risk.table.col = "strata") # color risk table
- Mutation differences between metastatic and non-metastatic patients for multiple genes in a Co-oncoplot.
#access the drug and radiation dataframes
rad <- clinical.BCRtab.all$clinical_radiation_skcm[-c(1,2),]
drug <- clinical.BCRtab.all$clinical_drug_skcm[-c(1,2),]#packages
library(maftools)#instantiate object
rna_query <- GDCquery(project ="TCGA-SKCM",
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "STAR - Counts")--------------------------------------
o GDCquery: Searching in GDC database
--------------------------------------
Genome of reference: hg38
--------------------------------------------
oo Accessing GDC. This might take a while...
--------------------------------------------
ooo Project: TCGA-SKCM
--------------------
oo Filtering results
--------------------
ooo By data.type
ooo By workflow.type
----------------
oo Checking data
----------------
ooo Checking if there are duplicated cases
ooo Checking if there are results for the query
-------------------
o Preparing output
-------------------GDCdownload(rna_query)Downloading data for project TCGA-SKCM
Of the 473 files for download 473 already exist.
All samples have been already downloadedrna_se <- GDCprepare(rna_query)
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|==============================================================================================|100% Completed after 30 s Starting to add information to samples
=> Add clinical information to samples
=> Adding TCGA molecular information from marker papers
=> Information will have prefix 'paper_'
skcm subtype information from:doi:10.1016/j.cell.2015.05.044
Available assays in SummarizedExperiment :
=> unstranded
=> stranded_first
=> stranded_second
=> tpm_unstrand
=> fpkm_unstrand
=> fpkm_uq_unstrand#set up maf object
maf_object <- read.csv("/Users/mishafaruki/Desktop/QBIO490/qbio_490_mishafaruki/analysis_data/skcm_clinical_data.csv")
colnames(clinical)[ colnames(clinical) == "bcr_patient_barcode" ] <- "Tumor_Sample_Barcode"
write.csv(clinical, "/Users/mishafaruki/Desktop/QBIO490/qbio_490_mishafaruki/analysis_data/skcm_clinical_data.csv", row.names = FALSE)
maf_query <- GDCquery(
project = "TCGA-SKCM",
data.category = "Simple Nucleotide Variation",
access = "open", # we only have access to somatic mutations which are open access
data.type = "Masked Somatic Mutation",
workflow.type = "Aliquot Ensemble Somatic Variant Merging and Masking")--------------------------------------
o GDCquery: Searching in GDC database
--------------------------------------
Genome of reference: hg38
--------------------------------------------
oo Accessing GDC. This might take a while...
--------------------------------------------
ooo Project: TCGA-SKCM
--------------------
oo Filtering results
--------------------
ooo By access
ooo By data.type
ooo By workflow.type
----------------
oo Checking data
----------------
ooo Checking if there are duplicated cases
ooo Checking if there are results for the query
-------------------
o Preparing output
-------------------# GDCdownload(maf_query) # you shouldn't have to redownload any data!
maf <- GDCprepare(maf_query) #as long as it runs, ignore any errorsWarning: One or more parsing issues, call `problems()` on your data frame for details, e.g.:
dat <- vroom(...)
problems(dat)maf_object <- read.maf(maf = maf,
clinicalData = rna_clinical,
isTCGA = TRUE)-Validating
-Silent variants: 126725
-Summarizing
--Possible FLAGS among top ten genes:
TTN
MUC16
-Processing clinical data
--Annotation missing for below samples in MAF:
TCGA-GN-A269
-Finished in 16.7s elapsed (7.215s cpu) #hold clinical data of maf object
maf_clinical = maf_object@clinical.data
maf_clinical#Extract data in definition column that is metastatic and put it into meta
meta <- maf_clinical[maf_clinical$definition == "Metastatic", ]
#Extract values from Tumor_Sample_Barcode of meta too and store it in there
meta <- meta$Tumor_Sample_Barcode
nonmeta <- maf_clinical[maf_clinical$definition == "Primary solid Tumor", ]
nonmeta <- nonmeta$Tumor_Sample_Barcode
# Subsetting based on clinical variables, meta and nonmeta
meta_maf <- subsetMaf(maf = maf_object, tsb = meta)--Possible FLAGS among top ten genes:
TTN
MUC16
-Processing clinical datanonmeta_maf <- subsetMaf(maf = maf_object, tsb = nonmeta)--Possible FLAGS among top ten genes:
TTN
MUC16
-Processing clinical datacoOncoplot(m1 = meta_maf,
m2 = nonmeta_maf,
m1Name = "Metastatic",
m2Name = "Non-metastatic",
borderCol = NA)
- Mutation differences for specific gene of interest (one of most mutated genes)(Co-lollipop plot)
#lollipop chart
lollipopPlot2(m1 = meta_maf,
m2 = nonmeta_maf,
m1_name = "Metastatic", #why can't this be m1Name?
m2_name = "Non-metastatic",
gene="TTN")Gene: TTN
Assuming protein change information are stored under column HGVSp_Short. Use argument AACol to override if necessary.
7 transcripts available. Use arguments refSeqID or proteinID to manually specify tx name.Using longer transcript NM_001267550 for now.
Assuming protein change information are stored under column HGVSp_Short. Use argument AACol to override if necessary.
7 transcripts available. Use arguments refSeqID or proteinID to manually specify tx name.Using longer transcript NM_001267550 for now.
lollipopPlot(maf=meta_maf,
gene="TTN")7 transcripts available. Use arguments refSeqID or proteinID to manually specify tx name.Using longer transcript NM_001267550 for now.
Assuming protein change information are stored under column HGVSp_Short. Use argument AACol to override if necessary.
lollipopPlot(maf=nonmeta_maf,
gene="TTN")7 transcripts available. Use arguments refSeqID or proteinID to manually specify tx name.Using longer transcript NM_001267550 for now.
Assuming protein change information are stored under column HGVSp_Short. Use argument AACol to override if necessary.
- Coocurrence or mutual exclusion of common gene mutations: one for metastatic patients, one for non-metastatic patients (2 somatic interation plots)
somatic_interaction_metastatic <- somaticInteractions(maf = meta_maf, top = 20)
somatic_interaction_nonmetastatic <- somaticInteractions(maf = nonmeta_maf, top = 20)
- Differential expression between non-metastatic and metastatic patients controlling for treatment effects, race, gender, and vital status (DESeq2 + Volcano plot)
- Treatments must include radiation, chemotherapy, immunotherapy, molecular therapy, vaccine
- Treatments must include radiation, chemotherapy, immunotherapy, molecular therapy, vaccine
#import packages
library(SummarizedExperiment)
BiocManager::install("DESeq2")'getOption("repos")' replaces Bioconductor standard repositories, see 'help("repositories", package = "BiocManager")' for
details.
Replacement repositories:
CRAN: https://cran.rstudio.com/
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/var/folders/td/fmyn6z116gxg422m8gll06th0000gn/T//RtmpQX4yvI/downloaded_packageslibrary(DESeq2)
library(ggplot2)
library(ggrepel)
BiocManager::install("EnhancedVolcano")'getOption("repos")' replaces Bioconductor standard repositories, see 'help("repositories", package = "BiocManager")' for
details.
Replacement repositories:
CRAN: https://cran.rstudio.com/
Bioconductor version 3.18 (BiocManager 1.30.22), R 4.3.2 (2023-10-31)
Warning: package(s) not installed when version(s) same as or greater than current; use `force = TRUE` to re-install:
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/var/folders/td/fmyn6z116gxg422m8gll06th0000gn/T//RtmpQX4yvI/downloaded_packageslibrary(EnhancedVolcano)all_chemo <- drug[drug$pharmaceutical_therapy_type == 'Chemotherapy',]
rna_clinical$chemotherapy <- ifelse(rna_clinical$patient %in% all_chemo$bcr_patient_barcode, 1, 0)
# immunotherapy
all_immuno <- drug[drug$pharmaceutical_therapy_type == 'Immunotherapy',]
rna_clinical$immunotherapy <- ifelse(rna_clinical$patient %in% all_immuno$bcr_patient_barcode, 1, 0)
# radiation
all_radio <- drug[drug$pharmaceutical_therapy_type == 'Ancillary',]
rna_clinical$radiation <- ifelse(rna_clinical$patient %in% all_radio$bcr_patient_barcode, 1, 0)
# molecular
all_molec <- drug[drug$pharmaceutical_therapy_type == 'Targeted Molecular therapy',]
rna_clinical$molecular <- ifelse(rna_clinical$patient %in% all_molec$bcr_patient_barcode, 1, 0)
# vaccine
all_vaccine <- drug[drug$pharmaceutical_therapy_type == 'Vaccine',]
rna_clinical$vaccine <- ifelse(rna_clinical$patient %in% all_vaccine$bcr_patient_barcode, 1, 0)
# Factorize all columns
rna_clinical$chemotherapy <- factor(rna_clinical$chemotherapy)
rna_clinical$immunotherapy <- factor(rna_clinical$immunotherapy)
rna_clinical$radiation <- factor(rna_clinical$radiation)
rna_clinical$molecular <- factor(rna_clinical$molecular)
rna_clinical$vaccine <- factor(rna_clinical$vaccine)
rna_clinical$race <- factor(rna_clinical$race)
rna_clinical$gender <- factor(rna_clinical$gender)
rna_clinical$vital_status <- factor(rna_clinical$vital_status)
# find counts of rna_se
countData <- assay(rna_se)
# create the DESeqDataSet
dds <- DESeqDataSetFromMatrix(countData = countData,
colData = rna_clinical,
design = ~ chemotherapy + immunotherapy + radiation + molecular + vaccine + race + gender + vital_status) Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]# Filter out genes with low expression
dds <- dds[rowSums(counts(dds)) > 20, ]
# Run DESeq
dds <- DESeq(dds)estimating size factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
final dispersion estimates
fitting model and testing
701 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTest
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
-- replacing outliers and refitting for 25273 genes
-- DESeq argument 'minReplicatesForReplace' = 7
-- original counts are preserved in counts(dds)
estimating dispersions
fitting model and testing
566 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTest
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]# Results with thresholding
res <- results(dds, alpha = 0.05)
res <- res[!is.na(res$padj) & abs(res$log2FoldChange) > 1, ]
# convert to data frame in order to pass to ggplot
res_df <- as.data.frame(res)
# volcano plot using the data frame
ggplot(res_df, aes(x=log2FoldChange, y=-log10(padj))) +
geom_point(aes(color = padj < 0.05)) +
scale_color_manual(values = c("TRUE" = "red", "FALSE" = "black")) +
theme_minimal() +
labs(x = "Log2 Fold Change", y = "-Log10 adjusted p-value", title = "Volcano plot of differential expression")
Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Cmd+Option+I.
When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Cmd+Shift+K to preview the HTML file).
The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.
---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter*. 

```{r setup}
#set up path 
  knitr::opts_knit$set(root.dir = normalizePath("/Users/mishafaruki/Desktop/QBIO490/qbio_490_mishafaruki/analysis_data")) 
```


```{r}
#import libraries 
library(BiocManager)
library(TCGAbiolinks)
library(maftools)

#create a query based on the clinical category of TCGA-SKCM 
clin_query <- GDCquery(project = "TCGA-SKCM",
  data.category = "Clinical",
  data.type = "Clinical Supplement",
  data.format = 'BCR Biotab')
  GDCdownload(clin_query)
  clinical.BCRtab.all <- GDCprepare(clin_query)

#create a query based on the Transcriptome Profiling/RNA category of TCGA-SKCM 
rna_query <- GDCquery(project ="TCGA-SKCM",
                      data.category = "Transcriptome Profiling",
                      data.type = "Gene Expression Quantification",
                      workflow.type = "STAR - Counts")

GDCdownload(rna_query)
#rna summarized experiment 
rna_se <- GDCprepare(rna_query)
```

1. Difference in survival between metastatic and non-metastatic patients (KM plot)
```{r}
# load required packages for KM plot 
if (!require(survival)) {
  install.packages("survival")
}

if (!require(survminer)) {
  install.packages("survminer")
}

if (!require(ggplot2)) {
  install.packages("ggplot2")
}

library(survival)
library(survminer)
library(ggplot2)

#create rna clinical that will contain data from colData
rna_clinical = rna_se@colData
#convert it to a dataframe 
rna_clinical <- as.data.frame(rna_clinical)
#Set output to Tumor_Sample_Barcode in rna_clincal so that it derived from patient category 
rna_clinical$Tumor_Sample_Barcode <- rna_clinical$patient

#Set output to vital_status_num have to contain vital_status colum's data but make the values numeric 
rna_clinical$vital_status_num <- as.numeric(rna_clinical$vital_status == "Dead")

#Create Survminer Object
#contains the survival data 
survival_object <- Surv(time = rna_clinical$days_to_death, event = rna_clinical$vital_status_num)
#fit survival_object curve 
fit <- survfit(surv_obj ~ rna_clinical$definition, data = rna_clinical)

#create KM plot 
ggsurvplot(fit, data = rna_clinical, pval = TRUE, 
           risk.table = TRUE, #risk table
           legend.title = "Metastatic Status",
           xlab = "Time", ylab = "Survival probability",
           break.time.by = 1000, # adjusting time intervals
           risk.table.col = "strata") #color risk table
```


2. Mutation differences between metastatic and non-metastatic patients for multiple genes in a Co-oncoplot.
```{r}
#access the drug and radiation dataframes
rad <- clinical.BCRtab.all$clinical_radiation_skcm[-c(1,2),]
drug <- clinical.BCRtab.all$clinical_drug_skcm[-c(1,2),]
```

```{r}
#packages
library(maftools)
```


```{r}
#instantiate object
rna_query <- GDCquery(project ="TCGA-SKCM",
                      data.category = "Transcriptome Profiling",
                      data.type = "Gene Expression Quantification",
                      workflow.type = "STAR - Counts")

GDCdownload(rna_query)
rna_se <- GDCprepare(rna_query)

```



```{r}
#set up maf object 
maf_object <- read.csv("/Users/mishafaruki/Desktop/QBIO490/qbio_490_mishafaruki/analysis_data/skcm_clinical_data.csv")
colnames(clinical)[ colnames(clinical) == "bcr_patient_barcode" ] <- "Tumor_Sample_Barcode"
write.csv(clinical, "/Users/mishafaruki/Desktop/QBIO490/qbio_490_mishafaruki/analysis_data/skcm_clinical_data.csv", row.names = FALSE)

maf_query <- GDCquery(
    project = "TCGA-SKCM", 
    data.category = "Simple Nucleotide Variation", 
    access = "open", # we only have access to somatic mutations which are open access
    data.type = "Masked Somatic Mutation", 
    workflow.type = "Aliquot Ensemble Somatic Variant Merging and Masking")

# GDCdownload(maf_query) # you shouldn't have to redownload any data!

maf <- GDCprepare(maf_query) #as long as it runs, ignore any errors

maf_object <- read.maf(maf = maf, 
                       clinicalData = rna_clinical,
                       isTCGA = TRUE)
```

```{r}
#hold clinical data of maf object 
maf_clinical = maf_object@clinical.data
maf_clinical
#Extract data in definition column that is metastatic and put it into meta 
meta <- maf_clinical[maf_clinical$definition == "Metastatic", ]
#Extract values from Tumor_Sample_Barcode of meta too and store it in there
meta <- meta$Tumor_Sample_Barcode

#Extract data in definition column that is Primary solid Tumor and put it into nonmeta 
nonmeta <- maf_clinical[maf_clinical$definition == "Primary solid Tumor", ]
#Extract values from Tumor_Sample_Barcode of nonmeta too and store it in there
nonmeta <- nonmeta$Tumor_Sample_Barcode

# Subsetting based on clinical variables, meta and nonmeta 
meta_maf <- subsetMaf(maf = maf_object, tsb = meta)
nonmeta_maf <- subsetMaf(maf = maf_object, tsb = nonmeta)

#create co-oncoplot 
coOncoplot(m1 = meta_maf,
           m2 = nonmeta_maf,
           m1Name = "Metastatic",
           m2Name = "Non-metastatic",
           borderCol = NA)
```
3. Mutation differences for specific gene of interest (one of most mutated genes)(Co-lollipop plot)
```{r}
#lollipop chart 
lollipopPlot2(m1 = meta_maf,
              m2 = nonmeta_maf,
              m1_name = "Metastatic", #why can't this be m1Name? 
              m2_name = "Non-metastatic",
              gene="TTN")
#single lollipop plot for meta 
lollipopPlot(maf=meta_maf, 
             gene="TTN")

#single lollipop plot for nonmeta 
lollipopPlot(maf=nonmeta_maf, 
             gene="TTN")
```

4. Coocurrence or mutual exclusion of common gene mutations: one for metastatic patients, one for non-metastatic patients (2 somatic interation plots)

```{r}
somatic_interaction_metastatic <- somaticInteractions(maf = meta_maf, top = 20)
somatic_interaction_nonmetastatic <- somaticInteractions(maf = nonmeta_maf, top = 20)
```

5. Differential expression between non-metastatic and metastatic patients controlling for treatment effects, race, gender, and vital status (DESeq2 + Volcano plot)
  a. Treatments must include radiation, chemotherapy, immunotherapy, molecular therapy, vaccine
  b. Treatments must include radiation, chemotherapy, immunotherapy, molecular therapy, vaccine

```{r}
#import packages 
library(SummarizedExperiment)
BiocManager::install("DESeq2")
library(DESeq2)
library(ggplot2)
library(ggrepel)
BiocManager::install("EnhancedVolcano")
library(EnhancedVolcano)
```

```{r}
chemoData <- drug[drug$pharmaceutical_therapy_type == 'Chemotherapy',]
rna_clinical$chemotherapy <- ifelse(rna_clinical$patient %in% chemoData$bcr_patient_barcode, 1, 0)

# immunotherapy
immunoData <- drug[drug$pharmaceutical_therapy_type == 'Immunotherapy',]
rna_clinical$immunotherapy <- ifelse(rna_clinical$patient %in% immunoData$bcr_patient_barcode, 1, 0)

# radiation
radiationData <- drug[drug$pharmaceutical_therapy_type == 'Ancillary',]
rna_clinical$radiation <- ifelse(rna_clinical$patient %in% radiationData$bcr_patient_barcode, 1, 0)

# molecular
molecularData <- drug[drug$pharmaceutical_therapy_type == 'Targeted Molecular therapy',]
rna_clinical$molecular <- ifelse(rna_clinical$patient %in% molecularData$bcr_patient_barcode, 1, 0)

# vaccine
vaccineData <- drug[drug$pharmaceutical_therapy_type == 'Vaccine',]
rna_clinical$vaccine <- ifelse(rna_clinical$patient %in% vaccineData$bcr_patient_barcode, 1, 0)




# Factorize all columns
rna_clinical$chemotherapy <- factor(rna_clinical$chemotherapy)
rna_clinical$immunotherapy <- factor(rna_clinical$immunotherapy)
rna_clinical$radiation <- factor(rna_clinical$radiation)
rna_clinical$molecular <- factor(rna_clinical$molecular)
rna_clinical$vaccine <- factor(rna_clinical$vaccine)

rna_clinical$race <- factor(rna_clinical$race)
rna_clinical$gender <- factor(rna_clinical$gender)
rna_clinical$vital_status <- factor(rna_clinical$vital_status)

# find counts of rna_se
countData <- assay(rna_se)

# create the DESeqDataSet
dds <- DESeqDataSetFromMatrix(countData = countData, colData = rna_clinical,
                              design = ~ chemotherapy + immunotherapy + radiation + molecular + vaccine + race + gender +vital_status)

# Filter out genes with low expression
dds <- dds[rowSums(counts(dds)) > 20, ]

# Run DESeq
dds <- DESeq(dds)

# Results with thresholding
res <- results(dds, alpha = 0.05)
res <- res[!is.na(res$padj) & abs(res$log2FoldChange) > 1, ]

# convert to data frame in order to pass to ggplot
res_df <- as.data.frame(res) 

# volcano plot using the data frame
ggplot(res_df, aes(x=log2FoldChange, y=-log10(padj))) + 
  geom_point(aes(color = padj < 0.05)) + 
  scale_color_manual(values = c("TRUE" = "red", "FALSE" = "black")) + 
  theme_minimal() + 
  labs(x = "Log2 Fold Change", y = "-Log10 adjusted p-value", title = "Volcano plot of differential expression")
```










Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Cmd+Option+I*.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Cmd+Shift+K* to preview the HTML file). 

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

